A Method for Real-time Detecting Freeway Moving Bottlenecks Using Intelligent Connected Vehicles

Aiming at the problem that the fixed-point detection method cannot effectively monitor the formation and evolution of the mobile bottleneck, a real-time detection method of the mobile bottleneck on the expressway based on intelligent networked vehicles is studied. A wavelet analysis-based method is proposed to reduce the errors of trajectories collected by intelligent connected vehicles (ICVs). And then the key points that represent the change of traffic states are identified based on the relationship between the vehicle trajectories and the traffic states. Considering that multiple traffic congestions may simultaneously occur on a road segment, an algorithm is proposed to classify the key points based on the space-time characteristics of traffic shockwaves. Finally, the traffic shockwave speed is calculated, and moving bottlenecks are identified and evaluated. Based on SUMO simulation platform, experiments are carried out on the detection effect of mobile bottleneck location, propagation speed and queuing delay under the proportion of various intelligent vehicles in Hujia freeway. The results show that when the penetration rate of ICVs is less than 10%, the accuracy of traffic wave speed estimation improves by an average of 20% after trajectory denoising. When the penetration rate exceeds 3%, the estimation error of the moving bottleneck propagation speed is below 0.42 m/s. When the penetration rate reaches 7%, the estimated position of the moving bottleneck has a deviation mostly within 10 m, with a maximum of 25 m. The proposed method can detect the presence of freeway bottlenecks which occur randomly and evaluate their severity in real-time.